面向不完整信息诊断推理的深度概率逻辑编程：以脑卒中检测为例
Deep probabilistic logic programming for diagnostic reasoning from incomplete information: A case study in stroke detection
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中文总结 AI 辅助
本研究以脑卒中检测为案例，提出结合最大熵技术与DeepProbLog的诊断系统构建方法，分析了不完整数据模型的性能及ProbFOIL 2压缩模型的潜力，拓展了神经符号方法在医疗诊断中的应用。
中文摘要 AI 辅助
在医疗应用中，原始数据常伴随严重的隐私问题，因此对文献中汇总统计量的编码尤为重要；另一方面，深度学习已成为基于视觉或听觉传感器数据评估症状的重要工具。DeepProbLog支持可扩展的神经符号方法，能在透明且严谨的概率框架（即分布语义下的概率逻辑编程）中容纳联结主义组件，用于分析患者图像。本研究以多模态数据的脑卒中检测为案例，探索从文献中可用的汇总统计量到基于DeepProbLog的诊断系统的路径，提出一种使用已确立的最大熵技术补全可用概率信息的工作流，以及使用概率逻辑编程系统ProbLog 2从熵最大化因果模型转换为可在DeepProbLog中表达的判别式神经符号模型的方法。本研究分析了来自较不完整数据的模型的相对性能，以及概率归纳逻辑编程系统ProbFOIL 2压缩大型判别式模型的潜力，并讨论了将DeepProbLog用于诊断推理的视角与意义。
英文摘要
In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the encoding of summary statistics from the literature. On the other hand, deep learning has become an invaluable tool for assessing symptoms based on visual or auditory sensor data. DeepProbLog allows for an extensible neuro-symbolic approach that accommodates connectionist components to analyse patient images within a transparent and rigorous probabilistic framework, namely probabilistic logic programming under the distribution semantics. Framed as a case study in stroke detection from multimodal data, this contribution explores the pathway from summary statistics available in the literature to a DeepProbLog-based diagnostic system. It suggests a workflow using established maximum entropy techniques to complete available probabilistic information and the probabilistic logic programming system ProbLog 2 to move from the entropy-maximising causal model to a discriminative neuro-symbolic model expressible within DeepProbLog. The relative performance of models derived from less complete data is analysed alongside the potential of the probabilistic inductive logic programming system ProbFOIL 2 for compressing large discriminative models, and the perspectives and implications of using DeepProbLog for diagnostic reasoning are discussed.
发表机构
- German University of Digital Science（德国数字科学大学）
机构由 AI 辅助整理，请以论文原文为准。
